Automatic selection and locking of intraoral images

ABSTRACT

A processing device receives an intraoral image of a first intraoral site and determines an identity of the first intraoral site. The processing device then locks the intraoral image and selects a portion of the intraoral image depicting a portion of the first intraoral site based at least in part on the identity of the first intraoral site. The processing device may then generate a model comprising the first intraoral site based at least in part on the locked intraoral image, wherein the portion of the locked intraoral image is used for a first region of the model, and wherein data from one or more additional intraoral images that also depict the portion of the first intraoral site is not used for the first region of the model.

TECHNICAL FIELD

Embodiments of the present invention relate to the field of intraoralscanning and, in particular, to a system and method for improving theresults of intraoral scanning.

BACKGROUND

in prosthodontic procedures designed to implant a dental prosthesis inthe oral cavity, the intraoral site at which the prosthesis is to beimplanted in many cases should be measured accurately and studiedcarefully, so that a prosthesis such as a crown, denture or bridge, forexample, can be properly designed and dimensioned to fit in place. Agood fit enables mechanical stresses to be properly transmitted betweenthe prosthesis and the jaw, and can prevent infection of the gums andtooth decay via the interface between the prosthesis and the intraoralsite, for example. The intraoral site may be scanned to providethree-dimensional (3D) data of the intraoral site. However, if the areaof a preparation tooth containing a finish line lacks definition, it maynot be possible to properly determine the finish line, and thus themargin of a restoration may not be properly designed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates one embodiment of a system for performing intraoralscanning and generating a virtual three-dimensional model of anintraoral site.

FIG. 2 illustrates a flow diagram for a method of automatically lockingan image set of an intraoral site, in accordance with embodiments of thepresent invention.

FIG. 3 illustrates a flow diagram for a method of locking multiple imagesets of one or more intraoral sites, in accordance with embodiments ofthe present invention.

FIG. 4 illustrates a flow diagram for a method of stitching togethermultiple image sets of one or more intraoral sites, in accordance withembodiments of the present invention.

FIG. 5 illustrates a flow diagram for a method of detecting an anomalyin an image set of an intraoral site and replacing the anomaly with datafrom an additional intraoral image, in accordance with embodiments ofthe present invention.

FIG. 6 illustrates a flow diagram for a method of extending a model ofan intraoral site where an incomplete edge is detected, in accordancewith embodiments of the present invention.

FIG. 7A illustrates a portion of an example dental arch during anintraoral scan session after a first set of intraoral images of apreparation tooth have been generated.

FIG. 7B illustrates the example dental arch of FIG. 7A during theintraoral scan session after a second set of intraoral images of a toothadjacent to the preparation tooth have been generated.

FIG. 7C illustrates the first set of intraoral images of FIG. 7A,wherein the first set of intraoral images includes an anomaly.

FIG. 7D illustrates a model created from the first set of intraoralimages of FIG. 7C with data from an additional intraoral image.

FIG. 7E illustrates a set intraoral images of a preparation tooth,wherein the set of intraoral images fails to capture all of thepreparation tooth.

FIG. 7F illustrates a model created from the first set of intraoralimages of FIG. 7E with data from an additional intraoral image.

FIG. 8 illustrates a block diagram of an example computing device, inaccordance with embodiments of the present invention.

DETAILED DESCRIPTION

Described herein are a method and apparatus for improving the quality ofscans, such as intraoral scans taken of intraoral sites (e.g., dentalsites) for patients. During a scan session, a user (e.g., a dentalpractitioner) of a scanner may generate multiple different images (alsoreferred to as scans) of an intraoral site, model of an intraoral site,or other object. The images may be discrete images (e.g.,point-and-shoot images) or frames from a video (e.g., a continuousscan). The practitioner may take a first set of intraoral images of afirst tooth after readying the first tooth for scanning. For example, ifthe first tooth is a preparation tooth (also referred to as apreparation), then one or more operations may be performed prior togenerating the first set of intraoral images to ensure that a quality ofthe first set of intraoral images is high. In one example, theseoperations briefly expose a finish line of the preparation tooth toensure that the finish line will show up in the first set of intraoralimages.

After completing the first set of intraoral images, the practitioner maytake additional sets of intraoral images of one or more adjacent teeth.The additional sets of intraoral images may include data for portions ofthe first tooth that was the focus of the first set of intraoral images.In some instances during creation of a 3D model using the intraoralimages, the data from the additional sets of intraoral images combineswith (e.g., is averaged with) the data for the first tooth from thefirst set of intraoral images to degrade a quality of that first toothin the 3D model.

In embodiments, to prevent the data from the additional sets ofintraoral images from degrading a quality of the first tooth in the 3Dmodel, the first set of images is automatically locked after the firstset of intraoral images is created. Additionally, portions of the firstset of intraoral images that depict the first tooth may be exclusivelyused for the generation of the 3D model of that first tooth. Thus, theadditional sets of intraoral images do not alter or add noise to aregion of the 3D model depicting the first tooth as a result of thefirst set of intraoral image being locked. In one embodiment, anidentity of the first tooth is determined, and the first portions of thefirst set of intraoral images are selected based at least in part on theidentity of the first tooth. Thus, the lower quality data from theadditional sets of intraoral images that depict the first tooth may notbe applied when generating the 3D model of the first tooth. This mayimprove an image quality of the first tooth in the 3D model of anintraoral site (e.g., of a portion of a jaw).

Embodiments described herein are discussed with reference to intraoralscanners, intraoral images, intraoral scan sessions, and so forth.However, it should be understood that embodiments also apply to othertypes of scanners than intraoral scanners. Embodiments may apply to anytype of scanner that takes multiple images and stitches these imagestogether to form a combined image or virtual model. For example,embodiments may apply to desktop model scanners, computed tomography(CT) scanners, and so forth. Additionally, it should be understood thatthe intraoral scanners or other scanners may be used to scan objectsother than intraoral sites in an oral cavity. For example, embodimentsmay apply to scans performed on physical models of an intraoral site orany other object. Accordingly, embodiments describing intraoral imagesshould be understood as being generally applicable to any types ofimages generated by a scanner, embodiments describing intraoral scansessions should be understood as being applicable to scan sessions forany type of object, and embodiments describing intraoral scanners shouldbe understood as being generally applicable to many types of scanners.

FIG. 1 illustrates one embodiment of a system 100 for performingintraoral scanning and/or generating a virtual three-dimensional modelof an intraoral site. In one embodiment, system 100 carries out one ormore operations described below with reference to FIGS. 2-6.

System 100 includes a computing device 105 that may be coupled to ascanner 150 and/or a data store 110. Computing device 105 may include aprocessing device, memory, secondary storage, one or more input devices(e.g., such as a keyboard, mouse, tablet, and so on), one or more outputdevices (e.g., a display, a printer, etc.), and/or other hardwarecomponents. The computing device 105 may be integrated into the scanner150 in some embodiments to improve performance and/or mobility.

Computing device 105 may be connected to data store 110 either directlyor via a network. The network may be a local area network (LAN), apublic wide area network (WAN) (e.g., the Internet), a private WAN(e.g., an intranet), or a combination thereof. Alternatively, data store110 may be an internal data store. Examples of network data storesinclude a storage area network (SAN), a network attached storage (NAS),and a storage service provided by a cloud computing service provider.Data store 110 may include a file system, a database, or other datastorage arrangement.

In some embodiments, a scanner 150 for obtaining three-dimensional (3D)data of an intraoral site in a patient's oral cavity is operativelyconnected to the computing device 105. Scanner 150 may include a probe(e.g., a hand held probe) for optically capturing three-dimensionalstructures (e.g., by confocal focusing of an array of light beams). Oneexample of such a scanner 150 is the iTero® intraoral digital scannermanufactured by Align Technology, Inc. Other examples of intraoralscanners include the 3M™ True Definition Scanner and the Apollo DIintraoral scanner and CEREC AC intraoral scanner manufactured bySirona®.

The scanner 150 may be used to perform an intraoral scan of a patient'soral cavity. An intraoral scan application 108 running on computingdevice 105 may communicate with the scanner 150 to effectuate theintraoral scan. A result of the intraoral scan may be one or more setsof intraoral images that have been discretely generated (e.g., bypressing on a “generate image” button of the scanner for each image).Alternatively, a result of the intraoral scan may be one or more videosof the patient's oral cavity. An operator may start recording the videowith the scanner 150 at a first position in the oral cavity, move thescanner 150 within the oral cavity to a second position while the videois being taken, and then stop recording the video. The scanner 150 maytransmit the discrete intraoral images or intraoral video (referred tocollectively as intraoral image data sets 135A-135N) to the computingdevice 105. Computing device 105 may store the intraoral image data sets135A-135N in data store 110. Alternatively, scanner 150 may be connectedto another system that stores the intraoral image data sets 135A-135N indata store 110. In such an embodiment, scanner 150 may not be connectedto computing device 105.

In one embodiment, intraoral scan application 108 includes an anomalyidentifying module 115, a flagging module 118, a model generation module125, an image locking module 128, an eraser module 132 and an expansionmodule 134. Alternatively, the operations of one or more of the anomalyidentifying module 115, flagging module 118, model generation module125, image locking module 128, eraser module 132 and/or expansion module134 may be combined into a single module or separated into furthermodules.

According to an example, a user (e.g., a practitioner) may subject apatient to intraoral scanning. In doing so, the user may apply scanner150 to one or more patient intraoral locations. The scanning may bedivided into one or more segments. As an example, the segments mayinclude a lower buccal region of the patient, a lower lingual region ofthe patient, a upper buccal region of the patient, an upper lingualregion of the patient, one or more preparation teeth of the patient(e.g., teeth of the patient to which a dental device such as a crown orother dental prosthetic will be applied), one or more teeth which arecontacts of preparation teeth (e.g., teeth not themselves subject to adental device but which are located next to one or more such teeth orwhich interface with one or more such teeth upon mouth closure), and/orpatient bite (e.g., scanning performed with closure of the patient'smouth with the scan being directed towards an interface area of thepatient's upper and lower teeth). Via such scanner application, thescanner 150 may provide image data (also referred to as scan data) tocomputing device 105. The image data may be provided in the form ofintraoral image data sets 135A-135N, each of which may include 2Dintraoral images and/or 3D intraoral images of particular teeth and/orregions of an intraoral site. In one embodiment, separate image datasets are created for the maxillary arch, for the mandibular arch, for apatient bite, and for each preparation tooth. Such images may beprovided from the scanner to the computing device 105 in the form of oneor more points (e.g., one or more pixels and/or groups of pixels). Forinstance, the scanner 150 may provide such a 3D image as one or morepoint clouds.

The manner in which the oral cavity of a patient is to be scanned maydepend on the procedure to be applied thereto. For example, if an upperor lower denture is to be created, then a full scan of the mandibular ormaxillary edentulous arches may be performed. In contrast, if a bridgeis to be created, then just a portion of a total arch may be scannedwhich includes an edentulous region, the neighboring preparation teeth(e.g., abutment teeth) and the opposing arch and dentition. Thus, thedental practitioner may input the identity of a procedure to beperformed into intraoral scan application 108. For this purpose, thedental practitioner may choose the procedure from a number of presetoptions on a drop-down menu or the like, from icons or via any othersuitable graphical user interface. Alternatively, the identity of theprocedure may be input in any other suitable way, for example by meansof preset code, notation or any other suitable manner, intraoral scanapplication 108 having been suitably programmed to recognize the choicemade by the user.

By way of non-limiting example, dental procedures may be broadly dividedinto prosthodontic (restorative) and orthodontic procedures, and thenfurther subdivided into specific forms of these procedures.Additionally, dental procedures may include identification and treatmentof gum disease, sleep apnea, and intraoral conditions. The termprosthodontic procedure refers, inter alia, to any procedure involvingthe oral cavity and directed to the design, manufacture or installationof a dental prosthesis at a dental site within the oral cavity(intraoral site), or a real or virtual model thereof, or directed to thedesign and preparation of the intraoral site to receive such aprosthesis. A prosthesis may include any restoration such as crowns,veneers, inlays, onlays, implants and bridges, for example, and anyother artificial partial or complete denture. The term orthodonticprocedure refers, inter alia, to any procedure involving the oral cavityand directed to the design, manufacture or installation of orthodonticelements at a intraoral site within the oral cavity, or a real orvirtual model thereof, or directed to the design and preparation of theintraoral site to receive such orthodontic elements. These elements maybe appliances including but not limited to brackets and wires,retainers, clear aligners, or functional appliances.

For many prosthodontic procedures (e.g., to create a crown, bridge,veneer, etc.), an existing tooth of a patient is ground down to a stump.The ground tooth is referred to herein as a preparation tooth, or simplya preparation. The preparation tooth has a finish line (also referred toas a margin line), which is a border between a natural (unground)portion of the preparation tooth and the prepared (ground) portion ofthe preparation tooth. The preparation tooth is typically created sothat a crown or other prosthesis can be mounted or seated on thepreparation tooth. In many instances, the finish line of the preparationtooth is below the gum line. While the term preparation typically refersto the stump of a preparation tooth, including the finish line andshoulder that remains of the tooth, the term preparation herein alsoincludes artificial stumps, pivots, cores and posts, or other devicesthat may be implanted in the intraoral cavity so as to receive a crownor other prosthesis. Embodiments described herein with reference to apreparation tooth also apply to other types of preparations, such as theaforementioned artificial stumps, pivots, and so on.

After the preparation tooth is created, a practitioner performsoperations to ready that preparation tooth for scanning. Readying thepreparation tooth for scanning may include wiping blood, saliva, etc.off of the preparation tooth and/or separating a patient's gum from thepreparation tooth to expose the finish line. In some instances, apractitioner will insert a cord around the preparation tooth between thepreparation tooth and the patient's gum. The practitioner will thenremove the cord before generating a set of intraoral scans of thepreparation tooth. The soft tissue of the gum will then revert back toits natural position, and in many cases collapses back over the finishline, after a brief time period. Accordingly, the practitioner uses thescanner 150 to scan the readied preparation tooth and generate a set ofintraoral images (e.g., intraoral image data set 135A) of thepreparation tooth before the soft tissue reverts back to its naturalposition.

After generating the set of intraoral images for the preparation tooth,the practitioner may preview the set of intraoral images (or a 3D modelcrated therefrom) to determine if the set of intraoral images havesatisfactory quality. The practitioner may then either rescan thepreparation tooth (or a portion thereof) if the quality is notsatisfactory, or may proceed to generate additional sets of intraoralimages (e.g., intraoral image sets 135B-135N) for adjacent teeth andother areas around the preparation tooth if the quality is satisfactory.These additional sets of intraoral images for the adjacent areas may betaken, for example, to ensure that a dental prosthesis will fit in thepatient's mouth. The additional sets of intraoral images may alsocapture portions of the preparation tooth after the gum has collapsedback over the finish line and/or after blood and/or saliva hasaccumulated on the preparation tooth.

Accordingly, in one embodiment after a first set of intraoral images(e.g., intraoral image data set 135A) is taken (e.g., of a preparationtooth), image locking module 128 automatically locks that first set ofintraoral images. The locked first set of intraoral images may beassociated with a preparation tooth of a patient. In one embodiment,image locking module 128 automatically locks image data sets associatedwith preparation teeth, but does not automatically lock other image datasets. Accordingly, image locking module 128 may determine whether a newimage data set is associated with a preparation tooth, and if so lockthat image data set.

The identity of the preparation tooth may be used by image lockingmodule 128 to automatically select portions of the locked first set ofintraoral images that will be applied for the preparation tooth in a 3Dmodel. Alternatively, a practitioner may use a graphical user interface(GUI) to mark the portions of the locked set of intraoral images thatwill be applied for the preparation tooth in the 3D model. In eithercase, the image locking module 128 may update the locked image data setso that only portions of the locked image data set that depict thepreparation tooth are locked, while the portions of the image data setthat depict gums, other teeth, etc. are unlocked. In one embodiment,image locking module 128 performs image processing to determine acontour of the preparation tooth as well as the finish line. All datarepresenting the preparation tooth inside of the finish line may belocked, while all data representing other intraoral features outside ofthe finish line may be unlocked. In one embodiment, a buffer is applied,and all data within the finish line plus the buffer is locked. Thebuffer may be, for example, a 1-3 mm offset from the finish line. Thus,image locking module 128 may algorithmically determine what data to keepin the locked image data set. Alternatively, a user may manuallydetermine what data to keep in the locked image data set. For example,the user may outline the area that he or she wishes to keep via thegraphical user interface. This locked image data set can later beunlocked at any time by a user.

The data from additional sets of intraoral images, which may alsoinclude lower quality depictions of the preparation tooth, may beignored by model generation module 125 during creation of thepreparation tooth in the 3D model. Thus, the finish line captured in thefirst image data set is not degraded by further image data.

In a further embodiment, additional intraoral image data sets may begenerated for additional preparation teeth and/or other teeth, such asteeth that are adjacent to a scanned preparation tooth. Image lockingmodule 128 may automatically (e.g., algorithmically and/or without userinput) lock some or all image data sets after the image data sets havebeen created and before additional intraoral images are taken. Imagelocking module 128 may assign each locked intraoral image data set135A-135N a separate layer or group identifier. These layers may be usedto refer to entire image data sets, and may be used to display or hideimage data sets and/or to prioritize data from image data sets forstitching together such image data sets.

In an example, a first image data set may be associated with a firsttooth and a second image data set may be associated with an adjacentsecond tooth. Data from the first image data set may overlap data fromthe second image data set, and may diverge from the data from the secondimage data set. To stitch together the image data sets, thediscrepancies between overlapping regions of an intraoral site depictedin these two image data sets should be remedied. One technique ofremedying the discrepancies is to average the data of the first imagedata set with the data of the second image data set for the overlappingregions. With the use of layers, a weight may be assigned to each imagedata set, and the averaging of the image data sets may be a weightedaverage. For example, if a user knows that data for a particularoverlapping region from a first image data set is superior in quality todata for the particular overlapping region of the second image data set,the user may select the first image data set as having a higherpriority. Model generation module 125 may then weight the first imagedata set more heavily than the second image data set when averaging thedifferences between the image data sets.

Image locking module 128 may associate each intraoral image data set135A-135N with a particular tooth and/or may otherwise identify anintraoral site associated with each intraoral image data set 135A-135N.In one embodiment, a user indicates which tooth he or she is scanningbefore generating an image data set. Alternatively, a user may firsttake an image data set, and may subsequently indicate a tooth imaged inthe image data set. In another implementation, intraoral scanapplication 108 may instruct the user to scan a particular tooth, andmay associate an identity of that particular tooth with the image dataset. Thus, each locked intraoral image data set 135A-135N may beassociated with a particular tooth, which may or may not be apreparation tooth.

When a scan session is complete (e.g., all images for an intraoral sitehave been captured), model generation module 125 may generate a virtual3D model of the scanned intraoral site. To generate the virtual 3Dmodel, model generation module 125 may register (i.e., “stitch”together) the intraoral imams generated from the intraoral scan session.In one embodiment, performing image registration includes capturing 3Ddata of various points of a surface in multiple images (views from acamera), and registering the images by computing transformations betweenthe images. The images may then be integrated into a common referenceframe by applying appropriate transformations to points of eachregistered image.

In one embodiment, image registration is performed for each pair ofadjacent or overlapping intraoral images (e.g., each successive frame ofan intraoral video). Image registration algorithms are carried out toregister two adjacent intraoral images, which essentially involvesdetermination of the transformations which align one image with theother. Image registration may involve identifying multiple points ineach image (e.g., point clouds) of an image pair, surface fitting to thepoints of each image, and using local searches around points to matchpoints of the two adjacent images. For example, model generation module125 may match points of one image with the closest points interpolatedon the surface of the other image, and iteratively minimize the distancebetween matched points. Model generation module 125 may also find thebest match of curvature features at points of one image with curvaturefeatures at points interpolated on the surface of the other image,without iteration. Model generation module 125 may also find the bestmatch of spin-image point features at points of one image withspin-image point features at points interpolated on the surface of theother image, without iteration. Other techniques that may be used forimage registration include those based on determining point-to-pointcorrespondences using other features and minimization ofpoint-to-surface distances, for example. Other image registrationtechniques may also be used.

Many image registration algorithms perform the fitting of a surface tothe points in adjacent images, which can be done in numerous ways.Parametric surfaces such as Bezier and B-Spline surfaces are mostcommon, although others may be used. A single surface patch may be fitto all points of an image, or alternatively, separate surface patchesmay be fit to any number of a subset of points of the image. Separatesurface patches may be fit to have common boundaries or they may be fitto overlap. Surfaces or surface patches may be fit to interpolatemultiple points by using a control-point net having the same number ofpoints as a grid of points being fit, or the surface may approximate thepoints by using a control-point net which has fewer number of controlpoints than the grid of points being fit. Various matching techniquesmay also be employed by the image registration algorithms.

In one embodiment, model generation module 125 may determine a pointmatch between images, which may take the form of a two dimensional (2D)curvature array. A local search for a matching point feature in acorresponding surface patch of an adjacent image may be carried out bycomputing features at points sampled in a region surrounding theparametrically similar point. Once corresponding point sets aredetermined between surface patches of the two images, determination ofthe transformation between the two sets of corresponding points in twocoordinate frames can be solved. Essentially, an image registrationalgorithm may compute a transformation between two adjacent images thatwill minimize the distances between points on one surface, and theclosest points to them found in the interpolated region on the otherimage surface used as a reference.

Model generation module 125 repeats image registration for all adjacentimage pairs of a sequence of intraoral images to obtain a transformationbetween each pair of images, to register each image with the previousone. Model generation module 125 then integrates all images into asingle virtual 3D model by applying the appropriate determinedtransformations to each of the images. Each transformation may includerotations about one to three axes and translations within one to threeplanes.

In many instances, data from one set of intraoral images does notperfectly correspond to data from another set of intraoral images. Foreach intraoral image data set 135A-135N, image locking module 128 mayuse the identity of the associated tooth to determine what portions ofthat image data set will be used exclusively for creation of aparticular region of a 3D model (e.g., for creation of the associatedtooth in the 3D model). The image locking module 128 may analyze theimage data in each intraoral image data set. For each image data set,the image locking module 128 may use stored information about anassociated tooth to determine from the analysis which portions or areasof that image data set represent that tooth and which portions or areasof that image data set represent other intraoral objects such as gumsand other teeth. The selection module 130 may then generate a contour ofthat tooth in the image data set. The generated contour may act as aborder. Data from the image data set that is within the contour may beexclusively used by model generation module 125 to generate the specificassociated tooth in a 3D model. Data from the image data set that isoutside of the contour may or may not be used to generate additionalfeatures or objects in the 3D model. Additionally, data outside thecontour may be combined with data from other image data sets to generatethe additional features or objects in the 3D model.

In one embodiment, the operation of contouring the tooth in a lockedimage data set is performed by image locking module (as describedabove). Image locking module 128 may then update the locked image dataset to lock portions of the image data set inside of the contour andunlock portions of the image data set outside of the contour.

Anomaly identifying module 115 is responsible for identifying anomaliesand/or other areas of interest (AOIs) from intraoral scan data (e.g.,intraoral images in an intraoral image data set) and/or virtual 3Dmodels generated from intraoral scan data. Such anomalies may includevoids (e.g., areas for which scan data is missing), areas of conflict orflawed scan data (e.g., areas for which overlapping surfaces of multipleintraoral images fail to match), areas indicative of foreign objects(e.g., studs, bridges, etc.), unclear margin line (e.g., margin line ofone or more preparation teeth), noisy information, and so forth. Anidentified void may be a void in a surface of an image. Examples ofsurface conflict include double incisor edge and/or otherphysiologically unlikely tooth edge, bite line shift, inclusion or lackof blood, saliva and/or foreign objects, differences in depictions of amargin line, and so on. The anomaly identifying module 115 may, inidentifying an anomaly, analyze patient image data (e.g., 3D image pointclouds) and/or one or more virtual 3D models of the patient alone and/orrelative to reference data 138. The analysis may involve direct analysis(e.g., pixel-based and/or other point-based analysis), the applicationof machine learning, and/or the application of image recognition. Suchreference data 138 may include past data regarding the at-hand patient(e.g., intraoral images and/or virtual 3D models), pooled patient data,and/or pedagogical patient data, some or all of which may be stored indata store 110.

Anomaly identifying module 115 to identify anomalies by performing imageprocessing to identify an unexpected shape, a region with low clarity, aregion missing data, color discrepancies, and so forth. Differentcriteria may be used to identify different classes of anomalies. In oneembodiment, an area of missing image data is used to identify anomaliesthat might be voids. For example, voxels at areas that were not capturedby the intraoral images may be identified. In one embodiment, anomalyidentifying module interpolates a shape for the anomaly based ongeometric features surrounding the anomaly and/or based on geometricfeatures of the anomaly (if such features exist). Such geometricfeatures may be determined by using edge detection, corner detection,blob detection, ridge detection, Hough transformations, structuretensors, and/or other image processing techniques.

The data regarding the at-hand patient may include X-rays, 2D intraoralimages, 3D intraoral images, 2D models, and/or virtual 3D modelscorresponding to the patient visit during which the scanning occurs. Thedata regarding the at-hand patient may additionally include past X-rays,2D intraoral images, 3D intraoral images, 2D models, and/or virtual 3Dmodels of the patient (e.g., corresponding to past visits of the patientand/or to dental records of the patient).

The pooled patient data may include X-rays, 2D intraoral images, 3Dintraoral images, 2D models, and/or virtual 3D models regarding amultitude of patients. Such a multitude of patients may or may notinclude the at-hand patient. The pooled patient data may be anonymizedand/or employed in compliance with regional medical record privacyregulations (e.g., the Health Insurance Portability and AccountabilityAct (HIPAA)). The pooled patient data may include data corresponding toscanning of the sort discussed herein and/or other data. The pedagogicalpatient data may include X-rays, 2D intraoral images, 3D intraoralimages, 2D models, virtual 3D models, and/or medical illustrations(e.g., medical illustration drawings and/or other images) employed ineducational contexts. The pedagogical patient data may include volunteerdata and/or cadaveric data.

Anomaly identifying module 115 may analyze patient scan data from laterin a patient visit during which the scanning occurs (e.g., one or morelater-in-the-visit 3D image point clouds and/or one or morelater-in-the-visit virtual 3D models of the patient) relative toadditional patient scan data in the form of data from earlier in thatpatient visit (e.g., one or more earlier-in-the-visit 3D image pointclouds and/or one or more earlier-in-the-visit virtual 3D models of thepatient). Anomaly identifying module 115 may additionally oralternatively analyze patient scan data relative to reference data inthe form of dental record data of the patient and/or data of the patientfrom prior to the patient visit (e.g., one or more prior-to-the-visit 3Dimage point clouds and/or one or more prior-to-the-visit virtual 3Dmodels of the patient). Anomaly identifying module 115 may additionallyor alternatively analyze patient scan data relative to pooled patientdata and/or pedagogical patient data.

Identifying of anomalies concerning missing and/or flawed scan data mayinvolve the anomaly identifying module 115 performing direct analysis,for instance determining one or more pixels or other points to bemissing from patient scan data and/or one or more virtual 3D models ofthe patient. Identification of anomalies concerning missing and/orflawed scan data may additionally or alternatively involve employingpooled patient data and/or pedagogical patient data to ascertain patientscan data and/or virtual 3D models as being incomplete (e.g., possessingdiscontinuities) relative to that which is indicated by the pooledpatient data and/or pedagogical patient data.

Flagging module 118 is responsible for determining how to present and/orcall out the identified anomalies. Flagging module 118 may provideindications or indicators of anomalies. The indications may be presented(e.g., via a user interface) to a user (e.g., a practitioner) inconnection with and/or apart from one or more depictions of teeth and/orgingivae of a patient (e.g., in connection with one or more X-rays, 2Dintraoral images, 3D intraoral images, 2D models, and/or virtual 3Dmodels of the patient). Indication presentation in connection withdepictions of patient teeth and/or gingivae may involve the indicationsbeing placed so as to correlate an indication with the correspondingportion of the teeth and/or gingivae.

The indications may be provided in the form of flags, markings,contours, text, images, and/or sounds (e.g., in the form of speech).Such a contour may be placed (e.g., via contour fitting) so as to followan extant tooth contour and/or gingival contour (e.g., as a border). Asan illustration, a contour corresponding to a void may be placed so asto follow a contour of the missing data. Such a contour may be placed(e.g., via contour extrapolation) with respect to a missing toothcontour and/or gingival contour so as to follow a projected path of themissing contour. As an illustration, a contour corresponding to missingtooth scan data may be placed so as to follow the projected path of thetooth portion which is missing, or a contour corresponding to missinggingival scan data may be placed so as to follow the projected path ofthe gingival portion which is missing.

Data for portions of the intraoral image data set that is within thecontoured anomaly may be unlocked or removed from the locked intraoralimage data set. The anomaly may be identified to a user, and the usermay then generate a new intraoral image that captures the area of theanomaly in the intraoral site. The portion of the new intraoral imagethat corresponds to an inside of the contour of the anomaly is then usedto replace the original data from the intraoral image data set for theanomaly. This data may then be added to the locked image data set. Thus,anomalies may be automatically detected in a set of intraoral images,and an additional intraoral image may be taken to overwrite the anomalywithout affecting the rest of the intraoral image data set.

In one embodiment, after anomaly identifying module 115 identifies ananomaly, anomaly identifying module 115 may then determine whether thereare any additional image data sets that include data covering the areaat which the anomaly was identified. Anomaly identifying module 115 maycompare this area from the one or more additional image data sets to thedata of the locked image data set. Based on this comparison, anomalyidentifying module 115 may determine that the data of the locked imagedata set covering the contour is to be replaced by data from anotherimage data set. The portion of the other image data set that correspondsto an inside of the contour of the anomaly may then be used to replacethe original data from the intraoral image data set for the anomaly.This data may then be added to the locked image data set.

In one embodiment, a different replacement option is presented to a userfor each additional image data set. Thus, for each additional image dataset, anomaly identifying module 115 may replace the anomaly with imagedata covering the contour of the anomaly from that additional image dataset. Each of the replacement options may be presented to the user, whomay then select which of the replacement options to apply. Once a userselection has been received, the data from the additional image data setassociated with the user selection may be used to replace the anomaly inthe locked image data set.

A 3D model created by model generation module 125 may be displayed to auser via a user interface of intraoral scan application. The 3D modelcan then be checked visually by the user. The user can virtuallymanipulate the 3D model via the user interface with respect to up to sixdegrees of freedom (i.e., translated and/or rotated with respect to oneor more of three mutually orthogonal axes) using suitable user controls(hardware and/or virtual) to enable viewing of the 3D model from anydesired direction. In addition to anomaly identifying module 115algorithmically identifying anomalies for rescan, a user may review(e.g., visually inspect) the generated 3D model of an intraoral site anddetermine that one or more areas of the 3D model are unacceptable.

Based on the inspection, the user may determine that part of the 3Dmodel is unsuitable or undesired, and that a remainder of the 3D modelis acceptable. The unacceptable portion of the 3D model can correspond,for example, to a part of a real dental surface of a scanned intraoralsite that was not sufficiently clearly defined in the 3D model. Forexample, during the initial 3D data collection step, for example viascanning, that resulted in the first 3D virtual model being generated,the corresponding part of the physical dental surface may have beencovered with foreign material, such as for example saliva, blood, ordebris. The corresponding part of the physical dental surface may alsohave been obscured by another element such as for example part of thegums, cheek, tongue, dental instruments, artifacts, etc. Alternatively,for example, during the initial 3D data collection step (e.g., viascanning) that resulted in the first 3D virtual model being generated,the unacceptable portion may be distorted or otherwise defective and maynot properly correspond to a physical dental surface (e.g., due to somedefect in the actual scanning process).

Via the user interface, a user may mark or otherwise demarcate theunacceptable portion of the 3D model. Eraser module 132 may then deleteor otherwise remove the marked portion from the 3D model (and theassociated portions of a locked image data set and/or other image dataset used to create the unacceptable portion). For example, the dentalprocedure of interest may be providing a dental prosthesis, and thedeleted or removed part of the 3D model may be part of a finish line ofa tooth preparation that exists in a real dental surface, but was notclearly represented in the 3D model (or in the intraoral image data sets135A-135N used to create the 3D model).

Intraoral scan application 108 may direct a user to generate one or moreadditional intraoral images of the dental site corresponding to theportion of the 3D model (and corresponding set or sets of intraoralimages) that was deleted or removed. The user may then use the scanner150 to generate the one or more additional intraoral images, which atleast partially overlaps with previously generated intraoral images. Theone or more additional intraoral images may be registered with the 3Dmodel (and/or with the intraoral image data sets used to create the 3Dmodel) to provide a composite of the 3D model and the one or moreadditional intraoral images. In the composite, the part of the 3D modelthat was previously deleted/removed is at least partially replaced witha corresponding part of the one or more additional intraoral images.However, the portions of the one or more additional images that areoutside of the deleted or removed part of the 3D model are not appliedto the composite or updated 3D model. In one embodiment, the portion ofthe new intraoral image that corresponds to the erased portion of alocked image data set is added to the locked image data set.

Expansion module 134 may perform operations similar to those of anomalyidentifying module 115 and/or eraser module 132. However, rather thanidentifying and correcting anomalies or unacceptable portions within a3D model, expansion module 134 may identify and/or correct portions ofthe 3D model at an edge of the 3D model. For example, an intraoral imagedata set 135A may have missed a portion of a tooth such that the toothis cut off in the 3D model (e.g., a portion of the tooth is not shown inthe 3D model). Expansion module 134 may algorithmically detect an edgeof the 3D model where a tooth appears to be clipped. Alternatively, auser may indicate via the user interface that a portion of the tooth isnot represented in the 3D model. The user may or may not mark a portionof edge of the 3D model where the model is incomplete.

A user may then use scanner 150 to generate one or more additionalintraoral images of the intraoral site (e.g., tooth) corresponding tothe area of the 3D model where data was missing. The one or moreadditional intraoral images may be registered to the 3D model. Expansionmodule 134 may then determine that a portion of the one or moreadditional intraoral images represents a region of the intraoral site(e.g., tooth) that was missing from the initial 3D model. This portionof the one or more additional intraoral images may then be added to the3D model to expand the 3D model for the intraoral site (e.g., tooth).Additionally, the portion of the one or more additional intraoral imagesmay be appended to a locked image data set.

In one embodiment, a practitioner may have generated a full or partialscan of one or more dental arches of a patient. At some time after thescan was completed, the patient may experience a change in dentalhealth, and may require a bridge or other prosthodontic to be appliedwhere there used to be a health tooth. In such an instance, the dentalpractitioner may leverage the previously completed scan. In particular,the practitioner may generate a preparation tooth, and may then scanthat preparation tooth to generate a locked intraoral image data set ofthe preparation tooth. This locked intraoral image data set may then becombined with the previously generated scan data to create a new 3Dmodel of the patient's dental arch. Most of the arch in the new 3D modelwill be based on data from the original scan, while the data for thepreparation tooth will be based on the locked image data set.

FIGS. 2-6 illustrate flow diagrams for methods of processing sets ofintraoral images and generating virtual 3D models therefrom. Thesemethods may be performed by processing logic that comprises hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (such as instructions run on a processing device), or acombination thereof. In one embodiment, processing logic corresponds tocomputing device 105 of FIG. 1 (e.g., to a computing device 105executing an intraoral scan application 108).

FIG. 2 illustrates a flow diagram for a method 200 of automaticallylocking an image set of an intraoral site, in accordance withembodiments of the present invention. At block 205 of method 200 anintraoral scan session is started. During the intraoral scan session, adental practitioner uses an intraoral scanner to create a set ofintraoral images focused on a particular intraoral site (e.g., focusedon a particular tooth). Processing logic may direct the dentalpractitioner as to which intraoral site (e.g., which tooth) is to bescanned or the dental practitioner may indicate which intraoral site isto be scanned or has been scanned. Alternatively, processing logic mayautomatically (e.g., algorithmically) identify the intraoral site basedon data from the set of intraoral images and/or based on one or moreadditional sets of intraoral images (e.g., that focus on other intraoralsites). At block 210, processing logic receives a set of intraoralimages of the intraoral site. At block 215, processing logic locks theintraoral image data set. This ensures that portions of the intraoralimage data set that depict particular areas of the intraoral site (e.g.,that depict a particular preparation tooth, including its margin line)will not later be modified or degraded by additional intraoral images.

Referring to FIG. 7A, a portion of an example dental arch 700 isillustrated during an intraoral scan session. The dental arch includestwo preparation teeth 708, 710 and adjacent teeth 704, 706, 712 as wellas a patient's gums 702. As shown, preparation teeth 708, 710 have beenground down to stumps so as to act as abutments and receive a bridge.Preparation tooth 708 includes a finish line 709 and preparation tooth710 includes a finish line 711. The illustrated finish lines 709, 711are above the gum line to improve visibility for this example. However,in many instances the finish lines are below the gum line. In oneexample, cord may have been packed between the preparation tooth 708 andsurrounding gums and then removed to cause the finish line 709 to bebriefly exposed for scanning.

An intraoral image data set 713 including intraoral image 714, intraoralimage 716 and intraoral image 718 is shown. Each of the intraoral images714-718 may have been generated by an intraoral scanner having aparticular distance from the dental surface being imaged. At theparticular distance, the intraoral images 714-718 have a particular scanarea and scan depth. The shape and size of the scan area will generallydepend on the scanner, and is herein represented by a rectangle. Eachimage may have its own reference coordinate system and origin. Eachintraoral image may be generated by a scanner at a particular position(scanning station). The location and orientation of scanning stationsmay be selected such that together the intraoral images adequately coveran entire target zone. Preferably, scanning stations are selected suchthat there is overlap between the intraoral images 714-718 as shown.Typically, the selected scanning stations will differ when differentscanners are used for the same target area, depending on the capturecharacteristics of the scanner used. Thus, a scanner capable of scanninga larger dental area with each scan (e.g., having a larger field ofview) will use fewer scanning stations than a scanner that is onlycapable of capturing 3D data of a relatively smaller dental surface.Similarly, the number and disposition of scanning stations for a scannerhaving a rectangular scanning grid (and thus providing projectedscanning areas in the form of corresponding rectangles) will typicallybe different from those for a scanner having a circular or triangularscanning grid (which would provide projected scanning areas in the formof corresponding circles or triangles, respectively). The intraoralimage data set 713 is locked automatically, and may be assigned to afirst layer.

Referring back to FIG. 2, at block 220 of method 200 portions of thefirst set of intraoral images are selected algorithmically by processinglogic. The selected portions may correspond to a contour of a tooth orother feature of the intraoral site. The selected portions may bedetermined based on performing image analysis and applying objectrecognition techniques, such as edge detection, edge matching, greyscalematching, gradient matching, bag of words models, and so on. Referencedata may be used to train processing logic to detect particular objectssuch as teeth. In one embodiment, the known identity of the tooth orintraoral site is used to assist the object detection process and toselect the portions of the intraoral images.

For example, in the example intraoral image data set 713 of FIG. 7A, acontour of the preparation tooth 708 may be generated. Ali portions ofthe intraoral images 714-718 of the intraoral image data set 713 thatare inside of the contour may be secured from further alteration. In oneembodiment, the locked image data set is updated so that the area insideof the contour is locked in the image data set and the area outside ofthe contour is unlocked.

At block 225 of method 200, processing logic receives one or moreadditional intraoral images that depict the intraoral site (e.g., thatdepict a tooth that was the focus of the locked set of intraoralimages). These additional intraoral images may be part of one or moreadditional intraoral image data sets for one or more additional teeth,for example. At block 230, processing logic generates a virtual 3D modelthat includes the intraoral site. The selected portions of the lockedintraoral image (e.g., that are inside of the determined contour) areused to create a first region of the model. For example, the selectedportions may be used to create a particular preparation tooth in the 3Dmodel. Data from the additional intraoral images are not used to createthe region of the 3D model.

Referring now to FIG. 7B, the example dental arch of FIG. 7A during theintraoral scan session is shown after a second intraoral image data set721 has been generated for a tooth 706 adjacent to the preparation tooth708. Intraoral image data set 721 includes intraoral images 722-728,which focus on adjacent tooth 706. However, as illustrated an area ofintraoral image 726 in the second intraoral image data set 721 alsodepicts preparation tooth 708 and finish line 709. However, since thefirst intraoral image data set 713 has been locked, data from the secondintraoral image data set 721 will not be used when creating a virtualrepresentation of preparation tooth 708 in a 3D model.

FIG. 3 illustrates a flow diagram for a method 300 of locking multipleimage sets of one or more intraoral sites, in accordance withembodiments of the present invention. At block 302 of method 300processing logic starts an intraoral scan session. At block 304,processing logic receives a first set of intraoral images of apreparation tooth. At block 306, processing logic determines an identityof the preparation tooth. At block 308, processing logic locks the firstset of intraoral images as a first layer.

At block 310, processing logic receives a second set of intraoral imagesof another tooth that is adjacent to the preparation tooth. The adjacenttooth may or may not be another preparation tooth. At block 312,processing logic determines an identity of the adjacent tooth. At block314, processing logic locks the second set of intraoral images as asecond layer.

At block 316, processing logic selects portions of the first set ofintraoral images. This selection may be made based at least in part onthe identity of the preparation tooth. Selecting the portions mayinclude contouring the preparation tooth in the first set of intraoralimages and selecting those portions that are within the contour. Atblock 318, processing logic selects portions of the second set ofintraoral images. This selection may be made based at least in part onthe identity of the adjacent tooth. Selecting the portions may includecontouring the adjacent tooth in the second set of intraoral images andselecting those portions that are within the contour.

At block 320, processing logic generates a virtual 3D model of anintraoral site that includes the preparation tooth, the adjacent toothand surrounding tissue.

Referring back to FIGS. 7A-7B, intraoral image data set 713 maycorrespond to the first set of intraoral images of the preparation toothin method 300. Similarly, intraoral image data set 721 may correspond tothe second set of intraoral images of the adjacent tooth in method 300.Accordingly, those portions of intraoral image data set 713 that depictadjacent tooth 706 would not be used to recreate the adjacent tooth inthe 3D model and those portions of intraoral image data set 721 thatdepict preparation tooth 708 would not be used to recreate thepreparation tooth in the 3D model.

FIG. 4 illustrates a flow diagram for a method 400 of stitching togethermultiple image sets of one or more intraoral sites, in accordance withembodiments of the present invention. Processing logic may receive afirst set if intraoral images (e.g., that focus on a preparation tooth)and a second set of intraoral images (e.g., that focus on an adjacenttooth or a full or partial arch). At block 405 of method 400, processinglogic identifies one or more discrepancies of overlapping data betweenthe first set of intraoral images and the second set of intraoralimages. For example, the first set of intraoral images and second set ofintraoral images may each depict the gums between the preparation toothand the adjacent tooth. However, the depictions of the gums in theseintraoral image data sets may not line up perfectly. For example, bloodand/or saliva may have accumulated on the gums between generation of thefirst intraoral image data set and the second intraoral image data set,or the positioning of the gums may be slightly different in the twointraoral image data sets. To create a 3D model that includes both thepreparation tooth and the adjacent tooth, the data from the twointraoral image data sets should be merged, and the conflicts in thedata remedied.

At block 410, processing logic receives an indication of priorities ofthe first set of images and the second set of images. For example, thefirst set of images may be known to have a higher quality or be moreimportant, and so a higher priority may be assigned to the first set ofimages. At block 415, processing logic uses the received indications ofpriority to prioritize the image data sets. At block 420, processinglogic applies a weighted average of the overlapping data between thefirst set of images and the second set of images to merge theoverlapping data. The weights that are applied to the image data setsmay be based on their priority. For example, the first image data setassigned the higher priority may be assigned a weight of 70% and thesecond set of intraoral images may be assigned a weight of 30%. Thus,when the data is averaged, the merged result will look more like thedepiction from the first image data set and less like the depiction fromthe second image data set.

In one embodiment, processing logic may render different versions of the3D model, each version showing a different prioritization and/ordifferent weightings of the intraoral image data sets. The user mayvisually inspect the different renderings and select which renderinglooks the most accurate. Processing logic may then prioritize the imagedata sets accordingly and then apply the appropriate weighted averageassociated with the user selection to create the 3D model.

FIG. 5 illustrates a flow diagram for a method 500 of correcting ananomaly in an intraoral image data set and/or in a 3D virtual modelgenerated from such an intraoral image data set, in accordance withembodiments of the present invention. The intraoral image data set maybe a set of discrete imams (e.g., taken from a point-and-shoot mode) ormultiple frames of an intraoral video (e.g., taken in a continuousscanning or video mode). The intraoral image data set may have been fora particular dental site (e.g., tooth) of a patient, and may be lookedto preserve the intraoral image data set.

At block 505 of method 500, processing logic identifies an anomalywithin the locked set of intraoral images and/or in a 3D model generatedfrom the locked set of intraoral images. The anomaly may be identifiedby performing image processing on the intraoral image data set andapplying a set of criteria thereto. In one embodiment, processing logicdetermines if any voxels or sets of voxels in the intraoral images or 3Dmodel satisfy the one or more criteria. Different criteria may be usedto identify different classes of anomalies. In one embodiment, missingimage data is used to identify anomalies that might be voids. Forexample, voxels at areas that were not captured by the intraoral imagesmay be identified.

At block 510, processing logic determines a border of the anomaly, suchas by generating a contour of the anomaly. In one embodiment, processinglogic interpolates a shape for the anomaly based on geometric featuressurrounding the anomaly and/or based on geometric features of theanomaly (if such features exist). For example, if the anomaly is a void,then the regions around the void may be used to interpolate a surfaceshape of the void. The shape of the anomaly may then be used to createthe contour. All data outside of the contour may remain locked andunchangeable, while data inside of the contour may be replaced with datafrom new intraoral images.

Processing logic may provide an indication of the anomaly via a userinterface. The contour of the anomaly may be displayed in manner tocontrast the anomaly from surrounding imagery. For example, teeth may beshown in white, while the anomaly may be shown in red, black, blue,green, or another color. Additionally or alternatively, an indicatorsuch as a flag may be used as an indication of the anomaly. Theindicator may be remote from the anomaly but include a pointer to theanomaly. The anomaly may be hidden or occluded in many views of theintraoral site. However, the indicator may be visible in all or manysuch views.

Referring to FIG. 7C, a set of intraoral images 730 is shown thatincludes a preparation tooth 708 having a finish line 709 and an anomaly732. As shown, the anomaly 732 has been contoured and has a particularshape (of an oval in this instance). The set of intraoral images 730includes intraoral images 714-718.

Referring back to FIG. 5, at block 515, processing logic receives anadditional image of the intraoral site. The additional image may includedata for the region of the 3D model or initial set of intraoral imageswhere the anomaly was detected. At block 520, processing logic updatesthe virtual 3D model based on replacing the data of the original set ofintraoral images within the border or contour with additional data fromthe additional image of the intraoral site. Thus, the anomaly may becorrected without affecting a remainder of the virtual 3D model.

Referring FIG. 7D, a virtual 3D model 740 created from the set ofintraoral images 730 and data from an additional intraoral image 709 isshown. The rendering of the preparation tooth 708 outside of the contourof the anomaly 742 is unaffected by image data from the additionalintraoral image 741. However, the portion of the preparation tooth 708that is inside the contour of the anomaly 742 is rendered based on datafrom the additional intraoral image 709.

FIG. 6 illustrates a flow diagram for a method 600 of extending a modelof an intraoral site where an incomplete tooth or other object isdetected, in accordance with embodiments of the present invention. Atblock 605 of method 600, processing logic determines that data for apreparation tooth (or other intraoral site) in a 3D model is incomplete.For example, processing logic may determine that an edge of thepreparation tooth has been cut off. This determination may be made, forexample, based on comparing an expected contour of the preparation toothor other tooth with a contour of the preparation tooth or other tooth ina computed 3D model. If the computed contour varies from the expectedcontour by more than a threshold amount, processing logic may determinethat the preparation tooth or other tooth is cut off in the model. Inone embodiment, such a determination is made responsive to a userindication that the preparation tooth or other tooth in the 3D model isincomplete. For example, a user may review the 3D model, determine thata portion of a tooth is cut off, and manually enter an expansion mode toadd data for the area that was cut off. Alternatively, such adetermination may be made algorithmically without first receiving userinput (e.g., based on performing image processing).

Referring to FIG. 7E, a set of intraoral images 750 includes intraoralimages 714 and 752. The set of intraoral images 750 depicts apreparation tooth 708 having a finish line 709. However, an edge 754 ofthe preparation tooth 708 is cut off.

At block 610, processing logic receives an additional intraoral image ofthe preparation tooth (or other tooth). At block 615, processing logicmay identify a border for the edge of the preparation tooth. In oneembodiment, this includes generating a contour of the edge of thepreparation tooth at the border. In some instances this may already havebeen performed at block 605. The shape of the edge may be used to createthe contour or border. All data inside of the contour may remain lockedand unchangeable.

At block 620, processing logic updates the model based on replacing dataoutside the border with additional data from the additional intraoralimage. Processing logic determines what portion of the additionalintraoral image of the preparation tooth depicts the portion of thepreparation tooth that was cut off in the initial set of intraoralimages (e.g., outside the border of the edge where data was cut off).The identified portion of the additional intraoral image may then beappended to the initial set of intraoral image and used to extend thepreparation tooth (or other tooth) in the model.

Referring to FIG. 7F, a virtual 3D model 760 created from the set ofintraoral images 750 of FIG. 7E with data from an additional intraoralimage 762 is shown. The rendering of the preparation tooth 708 inside ofa contour of the preparation tooth 708 is unaffected by image data fromthe additional intraoral image 762. However, a portion of the intraoralimage 762 showing the cut off region outside of the edge 754 is used toextend the preparation tooth 708 in the 3D model 760.

FIG. 8 illustrates a diagrammatic representation of a machine in theexample form of a computing device 800 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a Local Area Network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet computer, a set-topbox (STB), a Personal Digital Assistant (PDA), a cellular telephone, aweb appliance, a server, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines (e.g., computers)that individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein. The term machine shall also refer to an integrated all-in-onedevice that includes an intraoral scanner and a computing device (e.g.,scanner 150 and computing device 105 of FIG. 1).

The example computing device 800 includes a processing device 802, amain memory 804 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), astatic memory 806 (e.g., flash memory, static random access memory(SRAM), etc.), and a secondary memory (e.g., a data storage device 828),which communicate with each other via a bus 808.

Processing device 802 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 802 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 802may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 802 is configured to execute theprocessing logic (instructions 826) for performing operations and stepsdiscussed herein.

The computing device 800 may further include a network interface device822 for communicating with a network 864. The computing device 800 alsomay include a video display unit 810 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 812(e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and asignal generation device 820 (e.g., a speaker).

The data storage device 828 may include a machine-readable storagemedium (or more specifically a non-transitory computer-readable storagemedium) 824 on which is stored one or more sets of instructions 826embodying any one or more of the methodologies or functions describedherein. A non-transitory storage medium refers to a storage medium otherthan a carrier wave. The instructions 826 may also reside, completely orat least partially, within the main memory 804 and/or within theprocessing device 802 during execution thereof by the computer device800, the main memory 804 and the processing device 802 also constitutingcomputer-readable storage media.

The computer-readable storage medium 824 may also be used to store anintraoral scan application 850, which may correspond to the similarlynamed component of FIG. 1. The computer readable storage medium 824 mayalso store a software library containing methods for an intraoral scanapplication 850. While the computer-readable storage medium 824 is shownin an example embodiment to be a single medium, the term“computer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium other than a carrier wave that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent upon reading and understanding the above description. Althoughembodiments of the present invention have been described with referenceto specific example embodiments, it will be recognized that theinvention is not limited to the embodiments described, but can bepracticed with modification and alteration within the spirit and scopeof the appended claims. Accordingly, the specification and drawings areto be regarded in an illustrative sense rather than a restrictive sense.The scope of the invention should, therefore, be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A method, comprising: receiving an intraoralimage of a first intraoral site; determining an identity of the firstintraoral site; algorithmically performing the following by a processingdevice: locking the intraoral image; and selecting, based at least inpart on the identity of the first intraoral site, a portion of theintraoral image depicting a portion of the first intraoral site; andgenerating a model comprising the first intraoral site based at least inpart on the locked intraoral image, wherein the portion of the lockedintraoral image is used for a first region of the model, and whereindata from one or more additional intraoral images that also depict theportion of the first intraoral site is not used for the first region ofthe model.
 2. The method of claim 1, wherein the intraoral image is amember of a first set of intraoral images, the method furthercomprising: locking the first set of intraoral images; and for eachintraoral image that is a member of the first set of intraoral images,selecting a portion of that intraoral image depicting the portion of theintraoral site.
 3. The method of claim 2, further comprising: receivinga second set of intraoral images of a second intraoral site, wherein oneor more portions of the second set of intraoral images also depict thefirst intraoral site; and disregarding the one or more portions of thesecond set of intraoral images when generating the model, wherein thesecond set of intraoral images does not alter or add noise to the firstregion of the model as a result of the first set of intraoral imagesbeing locked.
 4. The method of claim 3, further comprising: determiningan identify of the second intraoral site; locking the second set ofintraoral images; selecting, based at least in part on the identity ofthe second intraoral site, one or more portions of the second set ofintraoral images depicting a portion of the second intraoral site,wherein the model further comprises the second intraoral site, andwherein the second intraoral site in the model is based at least in parton the locked second set of intraoral images.
 5. The method of claim 4,further comprising: stitching the first set of images to the second setof images, the stitching comprising: identifying one or morediscrepancies of overlapping data between the first set of images andthe second set of images; prioritizing the first set of images over thesecond set of images; and applying a weighted average of the overlappingdata between the first set of images and the second set of images,wherein data from the first set of images has a higher weight than datafrom the second set of images.
 6. The method of claim 3, wherein thelocked first set of intraoral images comprises a boundary for theselected portions of the first set of intraoral images, wherein the oneor more portions of the second intraoral set of intraoral images thatare inside of the boundary are not applied for the model, and whereinone or more additional portions of the second set of intraoral imagesthat are outside of the boundary are applied for the model.
 7. Themethod of claim 1, further comprising: receiving an indication of theidentity of the intraoral site, wherein the identity of the intraoralsite is determined based at least in part on the indication.
 8. Themethod of claim 1, wherein the locked intraoral image comprises ananomaly within the portion of the locked intraoral image, the methodfurther comprising: identifying the anomaly; determining a border of theanomaly; receiving an additional image of the first intraoral site; andupdating the model based on replacing data within the border from theintraoral image with additional data from the additional image.
 9. Themethod of claim 7, wherein the anomaly comprises at least one of a voidin the intraoral image, noise in the intraoral image, or unrealisticdata in the intraoral image.
 10. The method of claim 1, wherein theportion of the intraoral site comprises a preparation tooth of theintraoral site, the method further comprising: determining that data thepreparation tooth is incomplete; receiving an additional intraoral imageof the preparation tooth; identifying a border for an edge of thepreparation tooth where the data for the preparation tooth isincomplete; and updating the model based on replacing data outside theborder with additional data from the additional image to expand themodel at the edge.
 11. A non-transitory storage medium havinginstructions that, when executed by a processing device, cause theprocessing device to perform operations comprising: receiving a firstset of intraoral images of a preparation tooth and one or moreadditional intraoral objects, the first set of intraoral images showinga finish line of the preparation tooth, wherein the finish line is aboundary between a ground area of the preparation tooth and an ungroundarea of the preparation tooth; determining an identify of thepreparation tooth; performing the following by the processing devicewithout user input: locking the first set of intraoral images; andselecting, based at least in part on the identity of the preparationtooth, one or more portions of the set of intraoral images depicting thepreparation tooth; and generating a model comprising the preparationtooth based at least in part on the locked first set of intraoralimages, wherein the one or more portions of the locked first set ofintraoral images are used for the preparation tooth in the model, andwherein data from one or more additional intraoral images that alsodepicts the preparation tooth is not used.
 12. The non-transitorystorage medium of claim 11, the operations further comprising: receivinga second set of intraoral images of a second tooth adjacent to thepreparation tooth, wherein one or more portions of the second set ofintraoral images also depict the preparation tooth; and disregarding theone or more portions of the second set of intraoral images whengenerating the model, wherein the second set of intraoral images doesnot alter or add noise to the preparation tooth in the model as a resultof the first set of intraoral images being locked.
 13. Thenon-transitory storage medium of claim 12, the operations furthercomprising: determining an identify of the second tooth; locking thesecond set of intraoral images; and selecting, based at least in part onthe identity of the second tooth, one or more portions of the second setof intraoral images depicting the second tooth, wherein the modelfurther comprises the second tooth, and wherein the second tooth in themodel is based at least in part on the locked second set of intraoralimages.
 14. The non-transitory storage medium of claim 13, theoperations further comprising: stitching the first set of images to thesecond set of images, the stitching comprising: identifying one or morediscrepancies of overlapping data between the first set of images andthe second set of images; prioritizing the first set of images over thesecond set of images; and applying a weighted average of the overlappingdata between the first set of images and the second set of images,wherein data from the first set of images has a higher weight than datafrom the second set of images.
 15. The non-transitory storage medium ofclaim 12, wherein the locked first set of intraoral images comprises aboundary for the preparation tooth, wherein the one or more portions ofthe second set of intraoral images that are inside of the boundary arenot applied for the model, and wherein one or more additional portionsof the second set of intraoral images that are outside of the boundaryare applied for the model.
 16. The non-transitory storage medium ofclaim 11, the operations further comprising: receiving an indication ofthe identity of the preparation tooth, wherein the identity of thepreparation tooth is determined based at least in part on theindication.
 17. The non-transitory storage medium of claim 11, whereinthe locked first set of intraoral images comprises an anomaly within theone or more portions of the locked first set of intraoral images, theoperations further comprising: identifying the anomaly; determining aborder of the anomaly; receiving an additional image of the preparationtooth; and updating the model based on replacing data within the borderfrom the locked first set of intraoral images with additional data fromthe additional image.
 18. The non-transitory storage medium of claim 17,wherein the anomaly comprises at least one of a void in the intraoralimage, noise in the intraoral image, or unrealistic data in theintraoral image.
 19. The non-transitory storage medium of claim 11, theoperations further comprising: determining that data for the preparationtooth is incomplete; receiving an additional intraoral image of thepreparation tooth; identifying a border for an edge of the preparationtooth where the data for the preparation tooth is incomplete; andupdating the model based on replacing data outside the border withadditional data from the additional image to expand the model at theedge.
 20. A computing device comprising: a memory; and a processingdevice coupled to the memory, the processing device to: receive anintraoral image of a first intraoral site; determine an identify of thefirst intraoral site; perform the following without user input: lock theintraoral image; and select, based at least in part on the identity ofthe first intraoral site, a portion of the intraoral image depicting aportion of the first intraoral site; and generate a model comprising thefirst intraoral site based at least in part on the locked intraoralimage, wherein the portion of the locked intraoral image is used for afirst region of the model, and wherein data from one or more additionalintraoral images that also depict the portion of the first intraoralsite is not used for the first region of the model.